t-SNE, or t-Distributed Stochastic Neighbor Embedding, is a powerful technique used for visualizing high-dimensional data. It works by mapping data points into a two or three-dimensional space, allowing for easier interpretation and analysis. The algorithm focuses on preserving the local structure of the data, which means that similar data points remain close together in the lower-dimensional representation ¹.

One of the key features of t-SNE is its stochastic nature, meaning that the results can vary with each run of the algorithm. This variability can be attributed to the random initialization of the algorithm, which can lead to different visualizations for the same dataset ⁵. Additionally, while t-SNE is effective at maintaining local relationships, it may not always preserve the global structure of the data, which is an important consideration when interpreting the results ⁵.

t-SNE is widely used in various applications, particularly in the field of machine learning, where it is common to visualize features extracted from deep learning models ³. Overall, t-SNE is a valuable tool for data scientists and researchers looking to gain insights from complex datasets.